Sharing economy’s risky business
Sharing economy has lofty growth projections, with industry experts estimating that 30% of all miles traveled will be shared by 2030 [1]. The path to 30% seems evident, as daily news highlights sharing economy platforms that focus on delivering safe, cost-effective and convenient transportation solutions. However, the state of shared miles only reaches about 1% today [2].
So, how will the industry achieve exponential growth? By providing consumers with more access to shared transportation. And how does that happen? Well, sharing economy businesses need to grow. Seems obvious, right? But some companies fail to recognize data-driven risk levers as key to profitable growth. Risk levers help mobility companies scale globally, respond to changes in transportation, and balance costs.
And, while risk levers are surely being used to train how autonomous vehicles make decisions, their even more eminent application is to help sharing economy platforms make decisions about drivers. Here’s one example of how shared mobility statistics help you better understand and leverage your driver pool:
Taking a proactive approach with upfront insurance risk management
Sharing economy operators can take advantage of assessing insurance risk, before onboarding begins to benefit the company’s bottom line, and allow for reinvestment in growth initiatives.
Let’s game this out a bit with a quick example. Imagine you run a car share program with 100,000 drivers and vehicles already on the platform. By using the right predictive model to assess risk levels upfront (full disclosure, we built a great model at Arity to do just this), you open up a few different opportunities with the potential to A) reduce vehicle losses and B) build reserves through deposits.
A) Reduce vehicle losses
Applying key correlations from this model [3], you could reduce approximately 6.5% of loss costs per year by removing the riskiest 2% of drivers from the pool. If your current losses are $500/vehicle, this immediately opens up opportunities.
Original Losses/Year: 100,000 vehicles x $500 in losses/year = $50,000,000.00
Reduced Losses/Year: 100,000 vehicles x $467.50 in losses/year = $46,750,000.00
Savings in Annual Losses: $3,250,000.00
B) Build reserves through deposits
Now, let’s say that you need to retain the size of your driver pool and also want to offset the insurance risk associated with maintaining a larger pool. Since you know how to maneuver the lever of managing risk upfront, you can design a dynamic deposit based on subsets of your driving pool’s potential risk.
Deposit Structure: $500 deposit required from the riskiest 10%
$500 deposit x 10,000 sharing economy drivers = $5MM to offset potential losses of your riskiest drivers.
Through either approach, or even both, you can take advantage of upfront risk assessment to build reserves, allowing you to reallocate funds to attract new drivers and increase your market share.
Moreover, understanding the risk of specific subsets of your driving pool lets you proactively manage the trade off between the value and losses related to those specific drivers. If the riskiest drivers are also the highest-earning ones, then you need more detailed metrics to determine their lifetime value. Leveraging these insights enables you to manage insurance risk, while supporting your long-term business goals. This brings up the importance of another risk lever, using ongoing risk and driving data. But, more to come on that next time.
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References
[1] https://www.morganstanley.com/ideas/car-of-future-is-autonomous-electric-shared-mobility
[3] Based on the Arity PreQual(SM) model which is trained on data from Allstate Insurance. This graph represents the correlation between losses and only the riskiest 10% of the population.
